Digital images can be represented and stored in a variety of formats. A common feature in digital image representation formats is that the bits constituting an image file are divided into image description bits and header bits. Image description bits describe the actual underlying image. Often the image description bits are divided into smaller units for convenience. Header bits provide organizational information about the image, such as image size in pixels, file size, length in bits for the various smaller image description units, etc.
Compressed image files contain a wide variety of organizational information in the header primarily to facilitate convenient file management and interpretation. For example, in addition to conventional information such as width, height, color component information and other details, JPEG 2000 ITU-T Rec. T.800 (ISO/IEC 15444-1:2000) image headers also provide information about the number of bits contained in smaller units, such as groups of wavelet coefficients (termed code-blocks), that constitute compressed data for image and the wavelet-domain locations of these small units of coefficients. Other image file formats can contain similar information.
In R. De Queiroz and R. Eschbach, “Fast segmentation of the JPEG compressed documents,” Electronic Imaging, vol. 7, pp. 367-377, April 1998, segmentation of conventional JPEG compressed documents using the entropy of 8×8 blocks in the image is described. The technique described therein does not use header-based processing, as the entropy values are not available in the conventional JPEG image header. Also, the technique employs a discrete cosine transform (“DCT”) used by conventional JPEG that operates only on local 8×8 blocks. Hence, the technique does not use multi-scale transforms. Furthermore, the technique only uses the available entropy distributions on 8×8 blocks in the image domain and does not have access to any multi-scale bit distribution.
Image analysis involves describing, interpreting, and understanding an image. Image analysis extracts measurements, data or information from an image. Image analysis techniques involve feature extraction, segmentation and classification. Image analysis may be referred to as computer vision, image data extraction, scene analysis, image description, automatic photointerpretation, region selection or image understanding. See W. Pratt, Digital Image Processing, (2nd Edition), John Wiley & Sons, Inc., New York, N.Y., 1995, and A. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, N.J., 1995.
Image processing produces a modified output image from an input image. Image processing techniques include cropping, scaling, point operations, filtering, noise removal, restoration, enhancement. (Jain chapters 7 and 8; Pratt Part 4.)
In some applications, it is desirable for first perform image analysis on an image and then to use the analysis to control image processing on the image. For example, the program “pnmcrop” first analyzes an image to find stripes of a background color (a single color value, for example white or black) on all four sides. Then it performs an image processing operation, cropping, on the image to remove the stripes.